FL is also incorporated with a hierarchical blockchain to build a knowledge sharing in edge-based vehicular systems [1]}. The information of trained parameters can be shared over the IoV network consisting of RSUs and BSs which can be classified into separate groups based on their regional features under the management of a local blockchain running on a light-weight Proof-of-Knowledge (PoK) consensus mechanism. To realize intelligent distributed learning, an FL process is carried out among vehicles and RSUs in a certain area before sharing the trained model with a global FL network on the RUSs-BSs network. To achieve a secure blackIoT data collaboration among IoT users, an FLchain framework is also studied in [2]} among private data center, a public data center, and a blockchain network. Here, the data center aims to collect data from IoT environments based on FL which can exploit the computational resources of IoT devices to build a powerful model. Models with parameters are then stored in a distributed data storage system at the public data center with cryptography protection. To provide flexible and secure data management, blockchain is adopted based on a smart contract design, which can support access control over the data usage among users. Simulations with human movement datasets confirm high performance in training accuracy and high-security degrees for a wearable sensor network.
